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Mapping US and Texas Rail Lines

About

Mapping Rail Lines and Rail Nodes in the United States.

This is a precursor project to one that will create a graph of rail, shipping, trucking, and air freight transport networks for analysis.

Import Functions

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx

Reading Node Shape Data into gpd DF

shapefilepath = "/Users/jnapolitano/Projects/rail-mapping/North_American_Rail_Nodes/North_American_Rail_Nodes.shp"

node_df = gpd.read_file(shapefilepath)



node_df.head()

	OBJECTID	FRANODEID	COUNTRY	STATE	STFIPS	CTYFIPS	STCYFIPS	FRADISTRCT	PASSNGR	PASSNGRSTN	BNDRY	geometry
0	1	300000	US	HI	15	009	15009	7	None	None	0	POINT (-156.68973 20.93653)
1	2	300001	US	HI	15	009	15009	7	None	None	0	POINT (-156.68972 20.93916)
2	3	300002	US	HI	15	009	15009	7	None	None	0	POINT (-156.67932 20.88014)
3	4	300003	US	HI	15	009	15009	7	None	None	0	POINT (-156.67863 20.88102)
4	5	300004	US	AK	02	170	02170	8	None	None	0	POINT (-150.12336 62.39174)

Checking Cooridindate System.

The coordinate system must be in the epsg 3857 format to overlay.

node_df.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Converting to EPSG 3857 System

df_wm = node_df.to_crs(epsg=3857)

Plotting Every Rail node in the United States, Mexico, and Canada

ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
cx.add_basemap(ax, zoom=5)

Discussion of results Thus Far

Detail

Mapping every node in the data set produces too much noise to be useful. It is interesting though that the rail coverage in north america nearly covers the entire continent.

Next Steps

I will decrease the marker size to see if it improved the detail and usability of the plot.

Plotting Data with Smaller Markers

ax = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k', markersize = .5)
cx.add_basemap(ax, zoom=7)

Results

We have increased our detail to an acceptable level to understand just how interconnected the rail system is in North America. The data plotted thus far is a collection of all points. Next, we’ll review the data to identify points that may yield more interesting results

Filtering the Dataset

Printing the head of the dataframe for review

df_wm.head()

	OBJECTID	FRANODEID	COUNTRY	STATE	STFIPS	CTYFIPS	STCYFIPS	FRADISTRCT	PASSNGR	PASSNGRSTN	BNDRY	geometry
0	1	300000	US	HI	15	009	15009	7	None	None	0	POINT (-17442620.562 2384312.109)
1	2	300001	US	HI	15	009	15009	7	None	None	0	POINT (-17442620.281 2384624.961)
2	3	300002	US	HI	15	009	15009	7	None	None	0	POINT (-17441462.226 2377592.829)
3	4	300003	US	HI	15	009	15009	7	None	None	0	POINT (-17441385.698 2377697.091)
4	5	300004	US	AK	02	170	02170	8	None	None	0	POINT (-16711656.316 8952635.016)

Understanding the Fields

The data source does not detail the fields very well. https://geo.dot.gov/mapping/rest/services/NTAD/North_American_Rail_Nodes/MapServer/0

Unfortunately, we’ll have to infer their meaning.

Unique PASSNGR Values

My best guess is that these values designate nodes that serve passenger transport.

df_wm['PASSNGR'].unique()

array([None, 'A', 'C', 'B', 'b', 'R'], dtype=object)

Results

It is still not clear what these values translate to. I’ll have to continue investigating.

Unique PASSNGRSTN Values

My best guess is that these values designate nodes that serve as passenger transport terminals.

results = df_wm['PASSNGRSTN'].unique()
print(results)

[None 'Albany' 'Eugene' ... 'Chelsea' 'Rockport'
 '59th St. (U. of Chicago)']

Results

I think my guess is correct. These are definetely commmuter stations. I recognize Boston Landing and 59th ST. (U. of Chicago) from travel. I can’t assume however that these are only passenger stations. They may also be freight connections. Therefore, I cannot yet eliminate them from the dataset.

Unique FRADISTRICT

These values are associated with Federal Railroad Association Districts. I was able to find the the key for the values at https://railroads.dot.gov/divisions/regional-offices/safety-management-teams.

This will be useful to investigate the bottle necks by railway and safety zone, but still does not help me to completely eliminate commuter and passengers.

results = df_wm['FRADISTRCT'].unique()
print(results)

[ 7  8 99  6  5  4  3  2  1]

Attempting to Discern meaning from an Accompanying Dataset

I could not infer exactly what the node data means without the keys. I will have to look online for more information. In the mean time however I will map and explore the rail line data.

Loading the Rail Line Data

shapefilepath = "/Users/jnapolitano/Projects/rail-mapping/North_American_Rail_Lines/North_American_Rail_Lines.shp"

line_df = gpd.read_file(shapefilepath)



line_df.head()

	OBJECTID	FRAARCID	FRFRANODE	TOFRANODE	STFIPS	CNTYFIPS	STCNTYFIPS	STATEAB	COUNTRY	FRADISTRCT	...	CARDDIRECT	NET	MILES	KM	TIMEZONE	IM_RT_TYPE	DBLSTK	Shape_Leng	ShapeSTLen	geometry
0	1	300000	348741	348746	38	015	38015	ND	US	8	...	None	M	0.178101	0.286626	C	None	None	0.003194	418.656759	LINESTRING (-100.74521 46.80285, -100.74509 46...
1	2	300001	338567	338686	30	087	30087	MT	US	8	...	None	O	0.886585	1.426824	M	None	None	0.017227	2047.012225	LINESTRING (-106.65759 45.86405, -106.65769 45...
2	3	300002	330112	330117	16	031	16031	ID	US	8	...	None	M	0.221820	0.356985	M	None	None	0.004269	483.919572	LINESTRING (-113.78532 42.54225, -113.78342 42...
3	4	300003	330113	330116	16	031	16031	ID	US	8	...	None	I	0.127571	0.205306	M	None	None	0.002484	278.254150	LINESTRING (-113.78484 42.54231, -113.78482 42...
4	5	300004	329497	329504	16	063	16063	ID	US	8	...	None	M	0.447515	0.720207	M	DS	UP-DS	0.008412	982.887938	LINESTRING (-114.40776 42.93610, -114.40763 42...

5 rows × 39 columns

Checking Cooridindate System.

The coordinate system must be in the epsg 3857 format to overlay.

line_df.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Converting to EPSG 3857 System

line_wm = line_df.to_crs(epsg=3857)

Plotting Every Rail Line in the United States, Mexico, and Canada

ax = line_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k', markersize = .5)
cx.add_basemap(ax, zoom=7)

Printing Data Fields

line_wm.columns

Index(['OBJECTID', 'FRAARCID', 'FRFRANODE', 'TOFRANODE', 'STFIPS', 'CNTYFIPS',
       'STCNTYFIPS', 'STATEAB', 'COUNTRY', 'FRADISTRCT', 'RROWNER1',
       'RROWNER2', 'RROWNER3', 'TRKRGHTS1', 'TRKRGHTS2', 'TRKRGHTS3',
       'TRKRGHTS4', 'TRKRGHTS5', 'TRKRGHTS6', 'TRKRGHTS7', 'TRKRGHTS8',
       'TRKRGHTS9', 'DIVISION', 'SUBDIV', 'BRANCH', 'YARDNAME', 'PASSNGR',
       'STRACNET', 'TRACKS', 'CARDDIRECT', 'NET', 'MILES', 'KM', 'TIMEZONE',
       'IM_RT_TYPE', 'DBLSTK', 'Shape_Leng', 'ShapeSTLen', 'geometry'],
      dtype='object')

Creating a Map of Texas Rail Lines

Texas is an important freight destination. The state possess many natural important ports and freight stations.

Filtering the data set for Texas state Lines.

FIPS codes can be found at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/?cid=nrcs143_013696

Texas is recognized as code 48.

#Getting Unique values to identify 48 is in the set 
print(line_wm['STFIPS'].unique())
#48 is a strin in the dataset do not compare it to an integer!!
texas_lines_df = line_wm.loc[line_wm['STFIPS'] == '48']
#print(texas_lines_df)
#print(texas_lines_df)

['38' '30' '16' '41' '02' '56' '53' '46' '27' '31' '49' '19' '06' '32'
 '04' '08' '35' '29' '17' '20' '40' '05' '18' '21' '09' '44' '25' '23'
 '34' '33' '50' '36' '42' '51' '11' '10' '39' '24' '54' '37' '22' '48'
 '28' '47' '15' '01' '13' '45' '12' '26' '55' None '99']

Mapping Texas Non-Interactive

ax = texas_lines_df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k', markersize = .5)
cx.add_basemap(ax, zoom=6)

Mapping an Interactive Map

texas_lines_df.explore()

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United States Freight Overview

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Mapping US Shipping Routes and Ports